We also test four SOTA methods, i.e., Tracktor [1]}, UnsupTrack [2]}, MOT20_TBC [3]} and GCNNMatch [4]} on MOT20, combined with the proposed tracklet booster method.
Specifically, Tracktor is the same tracker as used in MOT17, UnsupTrack trains a ReID network to predict the generated labels using cross-entropy loss; MOT20_TBC jointly models detection, counting,
and tracking of multiple targets as a network flow program,
which simultaneously finds the global optimal detections and
trajectories of multiple targets over the whole video; GCNNMatch uses graph convolutional neural network based feature extraction and end-to-end feature matching for object association.
The results are reported in Table REF . Similarly, there are also large improvements on the IDF1 compared with the original methods. This demonstrates the effectiveness of the proposed method on the association task in MOT.
